Serves for topology-based computational analyses. scTDA realizes temporal studies and unbiased transcriptional regulation studies. It is an unsupervised statistical framework that can characterize transient cellular states. This tool can be used to any biological system responding to inductive cues or environmental perturbations like cellular differentiation processes such as hematopoiesis, the evolution of cancer cells, neurodegeneration, or developmental disorders.
Harnesses genetic variation to determine the genetic identity of each droplet containing a single cell. Demuxlet can detect droplets containing two cells from different individuals (doublets). It implements a statistical model for evaluating the likelihood of observing RNA-seq reads overlapping a set of single nucleotide polymorphisms (SNPs) from each cell- containing droplet.
A flexible statistical framework for the analysis of single-cell RNA sequencing data. MAST is suitable for supervised analyses about differential expression of genes and gene modules, as well as unsupervised analyses of model residuals, to generate hypotheses regarding co-expression of genes. MAST accounts for the bimodality of single-cell data by jointly modeling rates of expression (discrete) and positive mean expression (continuous) values. Information from the discrete and continuous parts is combined to infer changes in expression levels using gene or gene set-based statistics. Because our approach uses a generalized linear framework, it can be used to jointly estimate nuisance variation from biological and technical sources, as well as biological effects of interest.
Processes Chromium single cell 3’ RNA-seq output to align reads, generates gene-cell matrices and performs clustering and gene expression analysis. Cell Ranger combines Chromium-specific algorithms with the widely-used RNA-seq aligner STAR. It is delivered as a single, self-contained tar file that can be unpacked anywhere on the system. The tool includes four pipelines: cellranger mkfastq; cellranger count; cellranger aggr; cellranger reanalyze.
Serves for single-cell data analysis. Granatum is a program that provides biologists with access to single-cell bioinformatics methods, and software developers with the opportunity to promote and combine their tools with various others in customizable pipelines. Its architecture simplifies the incorporation of cutting-edge tools and enables handling of large datasets. Moreover, it can eliminate inter-module incompatibilities by isolating the dependencies of each module.
Processes scRNA-seq data and detects low quality cells, using a curated set of over 20 biological and technical features. cellity integrates a machine learning algorithm that allows the definition of a new type of low quality cells that cannot be detected visually and that can compromise downstream analyses. It allows users to process individual cells or apply the pipeline in parallel to process thousands of cells simultaneously.
Performs initial pre-processing and analysis of the droplet-based scRNA-seq data. DropEst in composed of three steps: (1) identifier parsing phase; (2) read mapping phase; and (3) filtering and quality control phase. It can characterize the quality of a library using a wide range of diagnostic indicators or filters out artefactual cellular barcodes. This tool provides extensive configuration options to accommodate alternative scRNA-seq protocol designs.
Allows quality control (QC) and analysis components of parallel single cell transcriptome and epigenome data. Dr.seq is a quality control (QC) and analysis pipeline that provides both multifaceted QC reports and cell clustering results. Parallel single cell transcriptome data generated by different technologies can be transformed to the standard input with contained functions. Using relevant commands, the software can also be used to report quality measurements based on four aspects and can generate detailed analysis results for scATAC-seq and Drop-ChIP datasets.
A method and software tool to detect technical artifacts in single-cell RNA-seq (scRNA-seq) samples by integrating both gene expression patterns and data quality information. SinQC assumes that if gene expression outliers are also associated with poor sequencing library quality (poor data quality, e.g., low mapped reads, low mapping rate or low library complexity), then they are more likely to be technical artifacts than to be cells with real biological variation. We apply SinQC to nine different scRNA-seq datasets, and show that SinQC is a useful tool for controlling scRNA-seq data quality.
An integrated software tool for quality filtering, normalization, feature selection, iterative dimensionality reduction, clustering and the estimation of gene-expression gradients from large ensembles of single-cell RNA-seq datasets. SCell is open source, and implemented with an intuitive graphical interface.
A statistical method and software to identify a sorted list of ordering effect (OE) genes. OEFinder is available as an R package along with user-friendly graphical interface implementations that allows users to check for potential artifacts in scRNA-seq data generated by the Fluidigm C1 platform.
A unified interface to matrix factorization algorithms and methods. Nimfa includes published matrix factorization algorithms, initialization methods, quality and performance measures and facilitates the combination of these to produce new strategies. Extensive documentation with working examples which demonstrate real applications, commonly used benchmark data and visualization methods are provided to help with the interpretation and comprehension of the results.
A generally applicable analytic pipeline for processing single-cell RNA-seq data from a whole organ or sorted cells. SINCERA provides a panel of analytic tools for users to conduct data filtering, normalization, clustering, cell type identification, and gene signature prediction, transcriptional regulatory network construction and important regulatory node identification. The pipeline enables RNA-seq analysis from heterogeneous single cell preparations after the nucleotide sequence reads are aligned to the genome of interest.
Addresses the lack of a comprehensive workflow for processing sequencing data from 3 prime end protocols. scPipe can deal with both unique molecular identifiers (UMIs) and sample barcodes, map reads to the genome and summarizes these results into gene-level counts. It implements a simple outlier-based method for discovering low quality cells and possible doublets to remove from further analysis.
A method to correct for cell growth in single-cell transcriptomics data. We derive the probability for the cell growth corrected mRNA transcript number given the measured, cell size dependent mRNA transcript number, based on the assumption that the average number of transcripts in a cell increases proportional to the cell's volume during cell cycle. cgCorrect can be used for both data normalization, and to analyze steady-state distributions used to infer the gene expression mechanism.
Supplies a platform for large scale scRNA-seq analysis. Scedar is an application able to handle large datasets to browse a dataset of interest, cluster cells, and determine cluster separating genes. This package can be used to process: (i) quality control and identification of cell outliers; (ii) visualization and; (iii) clustering. Besides, it can be customized and was developed to be integrated into external workflows.
Offers a method for managing 3’- end unique molecular identifiers (UMI)-based protocols. Sharq first removes and sorts low quality reads, maps the cleaned files to a reference genome and then performs a specific assignation that generates gene expression tables. The application is able to deal with UMIs and cell barcodes. It can be used for detecting wells where the amplification reaction failed, or to evaluate which cells contained sufficient material relative to an empty well background.
Manages cell free mRNA contamination within droplet based single cell RNA sequencing. SoupX is an R package able to evaluate, profile and subtract background contamination from a measured expression profile. The application computes which part of unique molecular identifiers (UMIs) for each cell can be related to the detected contamination to subsequently fit cells’ expression and lastly remove it.
Allows users to analyze and visualize RNA-Seq data. PIVOT furnishes four mains functionalities (i) a graphical interface that is able to wrap existing open source packages in a single user-interface (ii) multiple tools to manipulate datasets to perform derivation or normalization (iii) a way for allowing the compatibility between inputs and outputs from different analysis modules and, (iv) functions for automatically generate reports, publication-quality figures, and reproducible computations.
Provides a method for imputing missing values, and restoring the structure of the data. After the use of MAGIC, two- and three-dimensional gene interactions are restored. MAGIC is able to impute complex and non-linear shapes of interactions. MAGIC also retains cluster structure, enhances cluster-specific gene interactions and restores trajectories, as demonstrated in mouse retinal bipolar cells, hematopoiesis, and a generated epithelial-to-mesenchymal transition dataset.
Allows users to transform raw data from dropSeq/scrbSeq experiment to the final count matrix with QC plots. dropSeqPipe is an open source application that can perform five different tasks: (i) generate fastqc reports of the input data, (ii) obtain the final file for the aligned sorted data, (iii) produce plots based on pre-processing and alignement, (iv) create species plot, and (v) extract the expression data.
Contains useful tools for the analysis of single-cell gene expression data using the statistical software R. scater places an emphasis on tools for quality control, visualisation and pre-processing of data before further downstream analysis. scater enables the following: (i) automated computation of QC metrics; (ii) transcript quantification from read data with pseudo-alignment; (iii) data format standardisation; (iv) rich visualisations for exploratory analysis; (v) seamless integration into the Bioconductor universe; (vi) simple normalisation methods.
Visualizes transcriptome (RNA expression) data from hundreds of samples. Flotilla is a Python package. Flotilla is an open source, community-driven software written in Python that enables biologists with rudimentary knowledge of statistical methods and programming to analyze and visualize hundreds of RNA-seq datasets. This package includes interactive functions for common and important tasks in computational analyses of biological datasets such as dimensionality reduction, covariance analysis, classification, regression and outlier detection.
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